arXiv subjects

Panhe Hu

Publications and source records attributed to Panhe Hu.

7 recordsLinked to original sources

arXiv2025

Teaching large language models to see in radar: aspect-distributed prototypes for few-shot HRRP ATR

High-resolution range profiles (HRRPs) play a critical role in automatic target recognition (ATR) due to their richinformationregarding target scattering centers (SCs), which encapsulate the geometric and electromagnetic characteristics of thetarget.Under few-shot circumstances, traditional learning-based methods often suffer from overfitting and struggle togeneralizeeffectively. The recently proposed HRRPLLM, which leverages the in-context learning (ICL) capabilities of largelanguagemodels (LLMs) for one-shot HRRP ATR, is limited in few-shot scenarios. This limitation arises because it primarilyutilizesthe distribution of SCs for recognition while neglecting the variance of the samples caused by aspect sensitivity. Thispaperproposes a straightforward yet effective Aspect-Distributed Prototype (ADP) strategy for LLM-based ATRunder few-shotconditions to enhance aspect robustness. Experiments conducted on both simulated and measured aircraft electromagneticdatasets demonstrate that the proposed method significantly outperforms current benchmarks.

eess.SP↗

arXiv2025

Large Language Models Can Achieve Explainable and Training-Free One-shot HRRP ATR

This letter introduces a pioneering, training-free and explainable framework for High-Resolution Range Profile (HRRP) automatic target recognition (ATR) utilizing large-scale pre-trained Large Language Models (LLMs). Diverging from conventional methods requiring extensive task-specific training or fine-tuning, our approach converts one-dimensional HRRP signals into textual scattering center representations. Prompts are designed to align LLMs' semantic space for ATR via few-shot in-context learning, effectively leveraging its vast pre-existing knowledge without any parameter update. We make our codes publicly available to foster research into LLMs for HRRP ATR.

eess.SP↗

arXiv2024

A Deep Learning-Based Target Radial Length Estimation Method through HRRP Sequence

This paper introduces an innovative deep learning-based method for end-to-end target radial length estimation from HRRP (High Resolution Range Profile) sequences. Firstly, the HRRP sequences are normalized and transformed into GAF (Gram Angular Field) images to effectively capture and utilize the temporal information. Subsequently, these GAF images serve as the input for a pretrained ResNet-101 model, which is then fine-tuned for target radial length estimation. The simulation results show that compared to traditional threshold method and simple networks e.g. one-dimensional CNN (Convolutional Neural Network), the proposed method demonstrates superior noise resistance and higher accuracy under low SNR (Signal-to-Noise Ratio) conditions.

eess.SP↗

arXiv2024

HRRPGraphNet: Make HRRPs to Be Graphs for Efficient Target Recognition

High Resolution Range Profiles (HRRP) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of deep learning based HRRP recognition, these methods needs a large amount of training samples to generate good performance, which could be a severe challenge under non-cooperative circumstances. Currently, deep learning based models treat HRRP as sequences, which may lead to ignorance of the internal relationship of range cells. This letter introduces HRRPGraphNet, whose pivotal innovation is the transformation of HRRP data into a novel graph structure, utilizing a range cell amplitude(hyphen)based node vector and a range(hyphen)relative adjacency matrix. This graph(hyphen)based approach facilitates both local feature extraction via one(hyphen)dimensional convolution layers, global feature extraction through a graph convolution layer and a attention module. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet superior accuracy and robustness, particularly in limited training sample environments, underscoring the potential of graph(hyphen)driven innovations in HRRP(hyphen)based RATR.

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arXiv2023

Deep Intellectual Property Protection: A Survey

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields. The high performance of DNNs requires a huge amount of high-quality data, expensive computing hardware, and excellent DNN architectures that are costly to obtain. Therefore, trained DNNs are becoming valuable assets and must be considered the Intellectual Property (IP) of the legitimate owner who created them, in order to protect trained DNN models from illegal reproduction, stealing, redistribution, or abuse. Although being a new emerging and interdisciplinary field, numerous DNN model IP protection methods have been proposed. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of two mainstream DNN IP protection methods: deep watermarking and deep fingerprinting, with a proposed taxonomy. More than 190 research contributions are included in this survey, covering many aspects of Deep IP Protection: problem definition, main threats and challenges, merits and demerits of deep watermarking and deep fingerprinting methods, evaluation metrics, and performance discussion. We finish the survey by identifying promising directions for future research.

cs.AI↗

arXiv2022

A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification

Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic structure of the hyperspectral image, such as the physical noise generation. This would make these deep models unable to generate discriminative features and provide impressive classification performance. To leverage such intrinsic information, this work develops a novel deep learning framework with the noise inclined module and denoise framework for hyperspectral image classification. First, we model the spectral signature of hyperspectral image with the physical noise model to describe the high intraclass variance of each class and great overlapping between different classes in the image. Then, a noise inclined module is developed to capture the physical noise within each object and a denoise framework is then followed to remove such noise from the object. Finally, the CNN with noise inclined module and the denoise framework is developed to obtain discriminative features and provides good classification performance of hyperspectral image. Experiments are conducted over two commonly used real-world datasets and the experimental results show the effectiveness of the proposed method. The implementation of the proposed method and other compared methods could be accessed at https://github.com/shendu-sw/noise-physical-framework.

cs.CV↗

arXiv2019

Deep Manifold Embedding for Hyperspectral Image Classification

Deep learning methods have played a more and more important role in hyperspectral image classification. However, the general deep learning methods mainly take advantage of the information of sample itself or the pairwise information between samples while ignore the intrinsic data structure within the whole data. To tackle this problem, this work develops a novel deep manifold embedding method(DMEM) for hyperspectral image classification. First, each class in the image is modelled as a specific nonlinear manifold and the geodesic distance is used to measure the correlation between the samples. Then, based on the hierarchical clustering, the manifold structure of the data can be captured and each nonlinear data manifold can be divided into several sub-classes. Finally, considering the distribution of each sub-class and the correlation between different subclasses, the DMEM is constructed to preserve the estimated geodesic distances on the data manifold between the learned low dimensional features of different samples. Experiments over three real-world hyperspectral image datasets have demonstrated the effectiveness of the proposed method.

cs.CV↗